Sebastian Alberione
Senior Manager | Eng. | Tax

The video game industry is flourishing in Montréal and a source of great pride for the city, province and country. I interviewed a number of leaders of independent video game studios of all sizes to learn more about what drives this sector and talk about their journey, successes and challenges.

These discussions revealed that technology, in particular, game engines and digital distribution platforms, have had a major impact on the creation of numerous independent studios and the ability to develop and distribute quality games worldwide. They also revealed that, while the gaming industry’s entrepreneurial ecosystem has reached a high level of maturity, the general public is virtually unaware of the extent of outstanding game projects being developed in their city and the size and growth of the global video game industry (it will reach US$108.9 billion in 2017 according to a article). The popularity of eSports has also played a major role in these statistics.

Game engines

Studios can use software that handles most of the logic and interactions required to provide a 2D or 3D gaming experience, that is, tasks that previously required programmers are now automated. These engines can be combined with complementary technologies, such as ZBrush for 3D sculpting and painting, Maya for animation or Gamesparks/AWS for the infrastructure. As a result, small teams can quickly produce exceptional quality games and focus on the actual game creation process rather than on developing an engine. For example, take a look at the trailers for Sundered (by Lotus Games), a completely hand-drawn game or Mordheim (by Rogue Factor), an adaptation of the cult classic tabletop game.

Sometimes, integrated tools don’t necessarily meet all the needs of a studio, which will then develop proprietary technologies to expand its capabilities. This is the case, for example of Vandal Games, a Montréal studio specializing in multiplayer online games with a web browser, a technology that involved developing features that were not supported by the Unity engine. Other studios, such as Snowed In Studios in Ottawa, developed leading-edge expertise by developing technologies to meet specific needs that native features in existing engines could not.

The most widely used engines are Unity and Unreal. Unity’s numerous advantages include the ability to create and deploy games on more than 25 different platforms (mobile, console, PC, etc.), on the basis of monthly fees per workstation. Unreal’s business model is based on a percentage of sales on game distributions. A complete game can therefore be created on the platform at no cost. But there’s more to this industry!

Digital distribution platforms

New digital distribution platforms and multiplayer games such as XBOX Live, PS Network or Steam have revolutionized the industry. Not so long ago, boxed video games were sold on store shelves, creating a major entry barrier for small studios without access to the major players’ distribution channels. The situation has since changed, with the arrival of platforms such as Steam, since it only takes a few moments for anyone to distribute a game worldwide. And this is happening every day, with an increasing number of games becoming available.

This situation makes life more complicated for independent studios that aren’t able to undertake massive marketing campaigns. It’s not quite back to square one, but the game marketing budget is also a form of entry barrier. High quality games often go unnoticed as a result. Consider for example that 403 games were released on Steam in 2012, and over 6,000 to date in 2017.

Studios must often call on the services of an editor to take over the marketing component for a percentage of the profits. Finding an editor is not so easy however. According to Jean-François Boivin, of Panache Digital Games, the presence of local game editors with more involvement in independent studios would be very advantageous.

What’s next?

What new technological developments can we expect? It’s hard to say. Buzzwords aside, at this time, it does not appear that there will be major upheaval in terms of virtual or augmented reality. On the other hand, artificial intelligence is poised to expand considerably, both for the game mechanics and as a development tool (automation of complex creation processes, behaviour analyses, etc.).

According to Kien-Van Tram, from Vandal Games, progress in AI and the processing of megadata will make it possible to analyze all stages of a product’s life cycle and optimize player acquisition and retention and game monetization.

The main concern is the ability to identify games produced. There is certainly room for innovation in this area.

In closing, I’d like to thank the studios that took the time to talk with me. Their website links are presented below. Take a tour and discover their games!

For more information about independent studios, take a few moments to visit the Guilde des développeurs indépendants de jeu vidéo site (131 members).

Read a 2017 report by the Entertainment Software Association of Canada which provides some interesting facts about the Canadian video game industry.

04 Dec 2017  |  Written by :

M. Alberione is your expert in taxation for the Montréal office. Contact him today!

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November 2017

Would you like to reduce your income taxes? Although tax planning should be a year-long activity, there is still some time left to implement a few tax strategies that will help reduce your tax bill. Furthermore, there are certain new measures coming into effect beginning in 2018 that should be taken into consideration.

The following are a few simple, effective strategies that can be implemented before the end of 2017 or early in 2018. A few major changes that will soon come into effect are also highlighted. Don’t hesitate to contact your Raymond Chabot Grant Thornton advisor who can help you determine the measures that apply to your situation.

For more information, download this document (pdf)

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Online Tax Strategies, November 2017

Phasing Out of Restrictions on the Granting of Input Tax Refunds to Large Businesses as of January 1, 2018

In 2012, further to an agreement with the Government of Canada to harmonize the GST/HST and QST, the Government of Quebec announced that it would eliminate restrictions on the granting of input tax refunds (ITR) for large businesses. In the March 2015 Quebec budget, the Finance Minister announced that the restrictions would be phased out as of January 1, 2018.

On October 25, 2017, Revenue Quebec published QST bulletin TVQ. 206.1.10 to implement the new provisions and clarify the application of the Quebec Sales Tax Act (QSTA) with respect to the phase out of the restrictions starting on January 1, 2018 and ending on January 1, 2021.

Enterprises that are currently large businesses or will become one in the coming months or even in the next four years should be aware that the new measures will impact them and should have a system in place to closely monitor their ITR claims for QST purposes.

Download the document below.

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Canada, and specifically, Montréal, buzzes with massive investments and numerous initiatives in artificial intelligence (AI). This is why Raymond Chabot Grant Thornton started AI and advanced analytics practice this year to help SMEs take advantage of this latest technological revolution.

I’ll be leading the section on AI in the Tax-R&D practice’s newsletter. I’ll be delving into the questions you should be asking, practices to adopt, examples that could serve as inspirations for your business. I’ll also be looking at disruptive technologies (ones that displace established technologies and shake up the industry or ground-breaking products that create a completely new industry) that could improve, enhance or even revolutionize business practices.

Marvin Lee Minsky, one of the creators of AI, defined it as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition and decision-making. In this first article, we’ll be looking at using AI and machine learning to create agents (autonomous software) able to think and reason like humans.

What is machine learning?

Machine learning is a set of techniques (linear regression, Bayesian classification, boosting, neural networks, etc.) used to give a machine the ability to learn from past experiences so it can deduce rules that will form new knowledge and serve as a basis to analyze new situations. For example, using analytical data collected by ecommerce websites, a machine learning algorithm can determine the rules that characterize users most likely to delete their account. Using those rules, the algorithm can analyze users’ actions to offer promotions just before they would take the critical decision. If the calculations of former rules are systematically based on the matchings, set by experts, between analytical data and users, the learning process is said to be supervised. Semi-supervised learning occurs when rules’ calculations partially rely on matchings set by experts. When there are no matchings used in the calculations, the learning process is unsupervised. In the case of reinforced learning, the algorithm results are reused to guide the calculation of the next predictions.

A neural network is a network of computing units (neurons) operating in parallel and arranged in tiers to perform complex functions. Each successive tier uses the output received from the tier preceding it. When a neural network is used for visual recognition, the first tiers identify, for example, lines, curves and angles, the middle tiers identify shapes while the last tiers identify objects such as eyes or wheels. The resulting automatic learning method is known as deep learning. Although deep learning has existed from some 10 years, it has grown in leaps and bounds with the increase in computational power, in particular, the ability to use GPU-accelerated computing (graphics processing unit) for general processing and the advent of large databases.

State a machine learning problem

Machine learning can be used in business to resolve several problems. In order to state the problem properly, you need to determine to which of the four categories of machine learning algorithms it fits.

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Classification: The goal of problems in this category is to classify or label each object in a data set in supervised or semi-supervised mode, i.e. with the help of experts. They can be unary (e.g. can unusual client transactions be detected?), binary (e.g. will lead Doe be converted or not?) or multiple (e.g. what type of product is user John most likely to buy: computer, portable or smartphone?).

Clustering: The main difference with problems in this category is that there is no human intervention for determining the classification rules and classes. In order words, it is an unsupervised learning process. Problems in this category include portioning of users for marketing purposes (e.g., what are our main market segments, based on our clients’ demographics and their purchases?) or understanding their behaviour (e.g., how can we classify the key words used for searches in our website?).

Regression: This category includes problems to predict or calculate numeric values rather than classes. This includes price calculations (e.g. what is a fair sale price considering the various production constraints?), product demand predictions (e.g., considering the last marketing campaign, how many products will we sell next month?), etc.

Ranking: In this category, the importance of an object compared with other objects in the same data set is calculated. Examples include recommendations (what five products should be displayed for a user, based on purchasing history?), website layout (how should displays be organized on the website considering users’ browsing history?), etc.

So, the answer to the question in the title is YES. Artificial intelligence can be embedded into your business as long as you can state your problems in terms of the four categories. Initially, you should start with simple projects with considerable added value for your business. Stating the problem is all the more important because it will influence the data collection procedures you will need to implement, which we’ll be seeing in the forthcoming article, with emphasis on the quality and relevance of captured data.

01 Nov 2017  |  Written by :

Jean-François Djoufak, manager, taxation, Raymond Chabot Grant Thornton

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